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Record W2060944961 · doi:10.1260/0309-524x.39.2.175

Power Generation from Airflow Induced Vibrations

2015· article· en· W2060944961 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWind Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsAirflowVibrationMechanical energyAcousticsEnergy harvestingResonatorMechanicsPower (physics)Natural frequencyAmplitudeVoltageVortex-induced vibrationGenerator (circuit theory)Energy (signal processing)Materials sciencePhysicsElectrical engineeringEngineeringMechanical engineeringOptics

Abstract

fetched live from OpenAlex

This paper presents a coupled mechanical device that generates power by a direct conversion of the air flow into mechanical vibrations. The mechanism experiences a fluid force that changes with its orientation causing vibrations. The device consists of two tightly coupled parts: a mechanical resonator that produces high-frequency mechanical oscillations from quasi-steady airflow resulting in large amplitude vibrations and a piezoelectric power generator harvesting the energy from the resonator's motion. Assuming that the fluid-structure interactions solely depend on the instantaneous velocity, these interactions were studied using numerical modeling and wind-tunnel tests. The proposed energy harvester allows for locking up the device's lowest natural frequency to the vortex-shedding resonant frequency induced by the ambient energy source. The energy harvester demonstrated a peak-to-peak output voltage of 25V at 10Hz, from an input wind velocity of ∼7 m/s.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.709

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.035
GPT teacher head0.209
Teacher spread0.175 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it